Wireless digital networks provide users secure and cost-effective access to resources. Such wireless digital networks typically have a plurality of access points (AP) located throughout a designated area, by which the users can access the resources they desire. Wi-Fi networks operating in accordance with IEEE 802.11 standards are examples of such networks. The frequencies used by these networks are shared. They are shared not only among the wireless digital networks themselves, but also with other non-network devices. These shared frequencies face intermittent and continuous interference received from other non-network devices, including radio-frequency devices, such as microwave ovens, wireless video streaming devices, cordless telephones, and the like, as well as other adjacent wireless networking devices. Unfortunately, the effects of these types of interfering devices can vary. As an example, simply replacing or adding a microwave oven in a particular area where a particular wireless digital network is operating can dramatically alter the interference levels present within that particular wireless digital network.
To identify the sources of interference that obstruct operation of a wireless digital network, various types of test equipment and functionalities are used, for example, spectrum analyzers. Although sophisticated spectrum analyzers exist, which include receivers that may be calibrated to display and measure signals over a wide range of frequencies and amplitudes, such sophisticated devices are costly and often not used for continuous monitoring and management of wireless digital networks. Wireless digital networks contain access points (AP) and wireless client devices, both examples of narrowband network (radio) devices with spectrum data collection functionality.
A radio has the capability to receive Wi-Fi packets as well as non-Wi-Fi interference, which is received as Fourier transform samples. Not all Wi-Fi is received as Wi-Fi signals and not all non-Wi-Fi are received as Fourier transform samples. False detection may happen, for example, Wi-Fi packets may be received as FFT samples. Often, a majority of false detections of interferers are caused by the existence of Wi-Fi frames on the air that the radio cannot decode. For example, an 11n radio will not be able to decode wireless frames in compliance with IEEE 802.11ac standard and a 20 MHz radio will not be able to decode 40 MHz frames. Similarly, a radio that does not support optional formats such as STBC, LDPC, and Greenfield will not be able to decode these frames. In addition, frames on adjacent channels may also simply appear as “energy” on the channel. Since Wi-Fi frames may have large enough duration and the fixed FFT signature, these are highly likely to be classified as a fixed frequency or a frequency hopping interferer. Therefore, methods are required to filter out the FFTs that are generated by Wi-Fi frames in order to minimize false detections. In addition, the accurate classification of FFT signatures (non-Wi-Fi interferer vs. Wi-Fi) will also improve the accuracy of the channel utilization or duty cycle calculations.
Therefore, it is desirable to have enhanced ways for accurately classifying interferers as well as minimize the number of false detections.